SoroushVahidi/scidocs huggingface dataset:
Estimating 3D Hand Pose from a Cluttered Image
(2003)

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this cluttertolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.
Data Science , Predictive Analytics , and Big Data : A Revolution That Will Transform Supply Chain Design and Management
(2013)

W e illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.
An integrated charger using segmented windings of interior permanent magnet motor based on 3 phase with 9 windings
(2018)

Connecting the plug of electric vehicles (EVs) into the grid source directly means to charge the battery. The electric power generated from the grid goes through the electric motor, crosses the bidirectional inverter used as three-boost rectifier and is received by the load (battery). An innovative fast-integrated charger based on segmented windings of permanent magnet synchronous motor (PMSM) is introduced for plug-in electric vehicles (PEV). The three-phase with nine windings of PMSM is used during the traction and charging modes. Three (or single) phases of grid source are directly connected to the neutral points of segmented PMSM windings to provide high power density to the battery. The system configuration and operation of charging mode are explained in detail for proposed integrated on-board charger. The simulation models are designed by using Ansoft Maxwell, Ansys Maxwell circuit editor, and Matlab/Simulink softwares, and simulation results are presented to verify the performance of the proposed system. In addition, the experimental setup of the integrated system is under progress.
Query Optimization Techniques In Graph Databases
(2016)

Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for storing and managing data with graph-like structure. Today, they represent a requirementfor many applications that manage graph-like data,like social networks.Most of the techniques, applied to optimize queries in graph databases, have been used in traditional databases, distribution systems,... or they are inspired from graph theory. However, their reuse in graph databases should take care of the main characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to efficiently access data relationships. In this paper, we survey the query optimization techniques in graph databases. In particular,we focus on the features they have introduced to improve querying graph-like data.
Wait-Free Synchronization
(1991)

A wait-free implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a wait-free implementation of one data object from another lies at the heart of much recent work in concurrent algorithms, concurrent data structures, and multiprocessor architectures. First, we introduce a simple and general technique, based on reduction to a concensus protocol, for proving statements of the form, “there is no wait-free implementation of X by Y.” We derive a hierarchy of objects such that no object at one level has a wait-free implementation in terms of objects at lower levels. In particular, we show that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy: thay cannot be used to construct wait-free implementations of many simple and familiar data types. Moreover, classical synchronization primitives such astest&set and fetch&add, while more powerful than read and write, are also computationally weak, as are the standard message-passing primitives. Second, nevertheless, we show that there do exist simple universal objects from which one can construct a wait-free implementation of any sequential object.
MapReduce: Simplified Data Processing on Large Clusters
(2004)

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
(2005)

This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.
Adaptive median filters: new algorithms and results
(1995)

Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L(p) filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters.
AP3: cooperative, decentralized anonymous communication
(2004)

This paper describes a cooperative overlay network that provides anonymous communication services for participating users. The Anonymizing Peer-to-Peer Proxy (AP3) system provides clients with three primitives: (i) anonymous message delivery, (ii) anonymous channels, and (iii) secure pseudonyms. AP3 is designed to be lightweight, low-cost and provides "probable innocence" anonymity to participating users, even under a large-scale coordinated attack by a limited fraction of malicious overlay nodes. Additionally, we use AP3's primitives to build novel anonymous group communication facilities (multicast and anycast), which shield the identity of both publishers and subscribers.
Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey
(2011)

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.
A Successive Approximation Recursive Digital Low-Dropout Voltage Regulator With PD Compensation and Sub-LSB Duty Control
(2018)

This paper presents a recursive digital low-dropout (RLDO) regulator that improves response time, quiescent power, and load regulation dynamic range over prior digital LDO designs by 1–2 orders of magnitude. The proposed RLDO enables a practical digital replacement to analog LDOs by using an SAR-like binary search algorithm in a coarse loop and a sub-LSB pulse width modulation duty control scheme in a fine loop. A proportional-derivative compensation scheme is employed to ensure stable operation independent of load current, the size of the output decoupling capacitor, and clock frequency. Implemented in 0.0023 mm2 in 65 nm CMOS, the 7-bit RLDO achieves, at a 0.5-V input, a response time of 15.1 ns with a figure of merit of 199.4 ps, along with stable operation across a 20 000 $\times $  dynamic load range.
Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies
(2000)

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.
Exploring Synergies between Machine Learning and Knowledge Representation to Capture Scientific Knowledge
(2015)

In this paper we explore synergies between the machine learning and knowledge representation fields by considering how scientific knowledge is represented in these areas. We illustrate some of the knowledge obtained through machine learning methods, providing two contrasting examples of such models: probabilistic graphical models (aka Bayesian networks) and artificial neural networks (including deep learning networks). From knowledge representation, we give an overview of ontological representations, qualitative reasoning, and planning. Then we discuss potential synergies that would benefit both areas.
Coupling Matrix Synthesis for a New Class of Microwave Filter Configuration
(2004)

In this paper a new approach to the synthesis of coupling matrices for microwave filters is presente d. The new approach represents an advance on existing direct a nd optimization methods for coupling matrix synthesis in that it will exhaustively discover all possible coupling matrix solutions for a network if more than one exists. This enables a se lection to be made of the set of coupling values, resonator frequ ency offsets, parasitic coupling tolerance etc that will be best suited to the technology it is intended to realize the microwave filter with. To demonstrate the use of the method, the case of the r cently – introduced ‘extended box’ (EB) coupling matrix configuration is taken. The EB represents a new class of filter con figuration featuring a number of important advantages, one of which is the existence of multiple coupling matrix solutions for each prototype filtering function, eg 16 for 8 degree cases. This case is taken as an example to demonstrate the use of the synthesis method – yielding one solution suitable for dual-mode realiz ation and one where some couplings are small enough to neglect. Index Terms — Coupling matrix, filter synthesis, Groebner basis, inverted characteristic, multiple solutions.
An Artificial Bee Colony-Based COPE Framework for Wireless Sensor Network
(2016)

In wireless communication, network coding is one of the intelligent approaches to process the packets before transmitting for efficient information exchange. The goal of this work is to enhance throughput by using the intelligent technique, which may give comparatively better optimization. This paper introduces a biologically-inspired coding approach called Artificial Bee Colony Network Coding (ABC-NC), a modification in the COPE framework. The existing COPE and its variant are probabilistic approaches, which may not give good results in all of the real-time scenarios. Therefore, it needs some intelligent technique to find better packet combinations at intermediate nodes before forwarding to optimize the energy and maximize the throughput in wireless networks. This paper proposes ABC-NC over the existing COPE framework for the wireless environment.
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
(2010)

Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks.
What are mobile developers asking about? A large scale study using stack overflow
(2015)

The popularity of mobile devices has been steadily growing in recent years. These devices heavily depend on software from the underlying operating systems to the applications they run. Prior research showed that mobile software is different than traditional, large software systems. However, to date most of our research has been conducted on traditional software systems. Very little work has focused on the issues that mobile developers face. Therefore, in this paper, we use data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about. We employ Latent Dirichlet allocation-based topic models to help us summarize the mobile-related questions. Our findings show that developers are asking about app distribution, mobile APIs, data management, sensors and context, mobile tools, and user interface development. We also determine what popular mobile-related issues are the most difficult, explore platform specific issues, and investigate the types (e.g., what, how, or why) of questions mobile developers ask. Our findings help highlight the challenges facing mobile developers that require more attention from the software engineering research and development communities in the future and establish a novel approach for analyzing questions asked on Q&A forums.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
(2018)

Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion. The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations. Such information guides the feature learning process and preserves the modality relationships in both the common semantic space and the Hamming space. Extensive experiments carried out on three benchmark datasets validate that the proposed SSAH surpasses the state-of-the-art methods.
Arthrodesis with Intramedular Fixation in Posttraumatic Arthrosis of the Midfoot: A Case Report
(2014)

We present two middle-aged men with posttraumatic arthrosis of the midfoot. Both patients suffered from severe pain and their foot was unable to bear weight. Both were operated using new fusion bolt 6.5 mm and additional screws. In their case, arthrodesis was mandatory and effective intervention. After surgical treatment both patients were pain free and able to walk without crutches and return to daily work.
Wavelet analysis of EEG for seizure detection: Coherence and phasesynchrony estimation.
(2015)

This paper deals with the wavelet analysis method f or seizure detection in EEG time series and coherence estimation. The main part of the paper pr esents the basic principles of signal decomposition in connection with the EEG frequency ba nds. Wavelet analysis method has been used for detection of seizure onset. The wavelet fi ltered signal is used for the computation of spectral power ratio. The results show that our met hod can identify pre seizure, seizure, post seizure and non seizure phases. When dealing with s eizure detection and prediction problems it is important to identify seizure precursor dynamics and necessary to identify information about onset and spread of seizures. Therefore in th e second part the coherence and phase synchrony during pre seizure, seizure, post seizure an d non seizure are computed. We expect this method to provide more insight into dynamic aspects of the seizure generating process.
Evaluating Digital Forensic Options for the Apple iPad
(2011)

The iPod Touch, iPhone and iPad from Apple are among the most popular mobile computing platforms in use today. These devices are of forensic interest because of their high adoption rate and potential for containing digital evidence. The uniformity in their design and underlying operating system (iOS) also allows forensic tools and methods to be shared across product types. This paper analyzes the tools and methods available for conducting forensic examinations of the Apple iPad. These include commercial software products, updated methodologies based on existing jailbreaking processes and the analysis of the device backup contents provided by iTunes. While many of the available commercial tools offer promise, the results of our analysis indicate that most comprehensive examination of the iPad requires jailbreaking to perform forensic duplication and manual analysis of its media content.
Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies
(2016)

The HathiTrust Research Center (HTRC) is engaged in the development of tools that will give scholars the ability to analyze the HathiTrust digital library's 14 million volume corpus. A cornerstone of the HTRC's digital infrastructure is the workset -- a kind of scholar-built research collection intended for use with the HTRC's analytics platform. Because more than 66% of the digital corpus is subject to copyright restrictions, scholarly users remain dependent upon the descriptive accounts provided by traditional metadata records in order to identify and gather together bibliographic resources for analysis. This paper compares the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies by assessing their suitability for employment by the HTRC to meet scholars' needs. These include distinguishing among multiple versions of the same work; representing the complex historical and physical relationships among those versions; and identifying and providing access to finer grained bibliographic entities, e.g., poems, chapters, sections, and even smaller segments of content.